Streaming Analytics FAQ: What You Need to Know

Imagine two manufacturers—Manufacturer A and Manufacturer B—each competing for its share of the widget market.

Each relies on customer satisfaction and repeat business to succeed. Each has stringent quality control measures in place. But only Manufacturer A captures and analyzes streaming data to help assure quality. Only Manufacturer A has sensors that monitor everything from factory machine health to order fulfillment. Only Manufacturer A has sensor data pass through in-memory analytics queries on the fly, which helps the company find and fix defects before those defects find their way to customers.

Which of these two manufacturers is likely to have a competitive advantage?

These benefits of streaming analytics could help your business remain competitive, cut costs, and increase efficiency.

Q: What is streaming analytics? A: Streaming analytics, also called event stream processing, is the analysis of large, in-motion data called event streams. These streams comprise events that occur as the result of an action or set of actions, such as a financial transaction, equipment failure, or some other trigger. These triggers can be very granular, such as something that happens within a system at a point in time—a click, a sensor reading, a tweet or some other measurable activity. The growing number of connected devices—the Internet of Things—will exponentially increase the volume of events that surround business activity. The more data your business generates, the greater your potential benefits from streaming analytics.

Q: Why "streaming analytics?" Isn't traditional analytics enough? A: It doesn't have to be one or the other. Streaming analytics complements traditional analytics by adding real-time insight to your decision-making toolbox. In some circumstances, streaming analytics enables better business decisions by focusing on live, streaming data.

Traditional approaches rely on batch processing, where data is scored based on a schedule and may only process new data hourly, overnight or even weekly. These approaches are inherently reactive because they focus on aging information, which means businesses can only react to past events or conditions. Traditional architectures are limited because they have difficulty efficiently managing and tracking the consumption of event streams.

By contrast, SAS Event Stream Processing architecture can capture events, assess them, make decisions and share the outputs—all within specific time windows. It enables you to proactively respond to changing conditions, to improve operations, and to enhance customer interactions—all driven by real-time insights.

Q: Is streaming analytics right for my business? A: Not all organizations will benefit equally from streaming analytics. To decide whether you need the technology, consider the following:

What data streams do you currently have? Identify applications and devices that generate data in your organization and rank those streams according to importance. For example, think of two data streams at an oil refinery. One is generated by an employee HR portal and one comes from sensors installed on equipment. Because it could affect the refinery's mission as well as human health and safety, the sensor data cries out for real-time analysis while HR data may be a lower priority.

What will real-time analysis on those event streams do for the business? Data streams that are critical to your organization's business are probably good candidates for real-time analysis. Many enterprises find that real-time analysis on business-critical streams enhances their ability to respond to customers, identify market conditions, minimize safety risk and enhance security.

Q: What about open source streaming analytics platforms? A: As you evaluate open source streaming analytics options, make sure you consider the time to value. Do you have the in-house skill sets needed to get the most from the platform—or do you have the resources to hire workers with those skill sets? Open source analytics solutions often require a great deal of coding to deploy predictive models and to customize them for your environment. Many do not have a visual model building interface, which slows down the time to value, and they all vary on their interpretation of analytics in streams. These limitations can mean that it takes weeks or months for your organization to realize value from an open source platform.

SAS Event Stream Processing is a complete and integrated solution that enables rapid time to value. It provides an interactive GUI for visual model building, while also allowing programmers to write XML or interface through APIs. This capability creates a flexible model-building environment for users, and the SAS publisher/subscriber interface supports integration of this technology into your business.